This is the analysis of the Ivermectin arm of the PLATCOV study
Data preparation is done in a different R script called data_prep.R. This Markdown script assumes that the data are saved in a .csv file Ivermectin_analysis.csv in long format. The intention to treat population (all randomised patients included in this analysis) The file Ivermectin_analysis.csv contains the patient clinical and viral load data with the following column headers:
## _
## platform x86_64-apple-darwin17.0
## arch x86_64
## os darwin17.0
## system x86_64, darwin17.0
## status
## major 4
## minor 0.2
## year 2020
## month 06
## day 22
## svn rev 78730
## language R
## version.string R version 4.0.2 (2020-06-22)
## nickname Taking Off Again
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] plotrix_3.8-2 dplyr_1.0.7 reshape2_1.4.4
## [4] tictoc_1.0.1 censReg_0.5-32 maxLik_1.5-2
## [7] miscTools_0.6-26 RColorBrewer_1.1-2 loo_2.4.1
## [10] rstanarm_2.21.1 Rcpp_1.0.7 lme4_1.1-27.1
## [13] Matrix_1.3-4 rstan_2.21.2 ggplot2_3.3.5
## [16] StanHeaders_2.21.0-7
##
## loaded via a namespace (and not attached):
## [1] plm_2.6-0 minqa_1.2.4 colorspace_2.0-2
## [4] ellipsis_0.3.2 ggridges_0.5.3 rsconnect_0.8.24
## [7] markdown_1.1 base64enc_0.1-3 rstudioapi_0.13
## [10] glmmML_1.1.1 DT_0.19 fansi_0.5.0
## [13] codetools_0.2-18 splines_4.0.2 knitr_1.34
## [16] shinythemes_1.2.0 bayesplot_1.8.1 Formula_1.2-4
## [19] jsonlite_1.7.2 nloptr_1.2.2.2 shiny_1.6.0
## [22] compiler_4.0.2 backports_1.2.1 assertthat_0.2.1
## [25] fastmap_1.1.0 cli_3.0.1 later_1.3.0
## [28] htmltools_0.5.2 prettyunits_1.1.1 tools_4.0.2
## [31] igraph_1.2.6 gtable_0.3.0 glue_1.4.2
## [34] V8_3.4.2 jquerylib_0.1.4 vctrs_0.3.8
## [37] nlme_3.1-153 crosstalk_1.1.1 lmtest_0.9-38
## [40] xfun_0.26 stringr_1.4.0 rbibutils_2.2.7
## [43] ps_1.6.0 collapse_1.7.6 mime_0.11
## [46] miniUI_0.1.1.1 lifecycle_1.0.0 gtools_3.9.2
## [49] MASS_7.3-54 zoo_1.8-9 scales_1.1.1
## [52] colourpicker_1.1.0 promises_1.2.0.1 parallel_4.0.2
## [55] sandwich_3.0-1 inline_0.3.19 shinystan_2.5.0
## [58] yaml_2.2.1 curl_4.3.2 gridExtra_2.3
## [61] sass_0.4.0 bdsmatrix_1.3-4 stringi_1.7.4
## [64] dygraphs_1.1.1.6 checkmate_2.0.0 boot_1.3-28
## [67] pkgbuild_1.2.0 Rdpack_2.1.3 rlang_0.4.11
## [70] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.14
## [73] lattice_0.20-44 purrr_0.3.4 rstantools_2.1.1
## [76] htmlwidgets_1.5.4 processx_3.5.2 tidyselect_1.1.1
## [79] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
## [82] generics_0.1.0 DBI_1.1.1 pillar_1.6.2
## [85] withr_2.4.2 xts_0.12.1 survival_3.2-13
## [88] tibble_3.1.4 crayon_1.4.1 utf8_1.2.2
## [91] rmarkdown_2.11 grid_4.0.2 callr_3.7.0
## [94] threejs_0.3.3 digest_0.6.27 xtable_1.8-4
## [97] httpuv_1.6.3 RcppParallel_5.1.4 stats4_4.0.2
## [100] munsell_0.5.0 bslib_0.3.0 shinyjs_2.0.0
## ITT population:
##
## Favipiravir Fluoxetine Ivermectin No study drug Regeneron
## 45 3 46 45 40
## Remdesivir
## 45
## These IDs are in the ITT database but are not in the PCR database:
## [1] "PLT-TH1-003" "PLT-TH1-005" "PLT-TH1-006" "PLT-TH1-012" "PLT-TH1-013"
## [6] "PLT-TH1-016" "PLT-TH1-019" "PLT-TH1-022" "PLT-TH1-025" "PLT-TH1-027"
## [11] "PLT-TH1-029" "PLT-TH1-030" "PLT-TH1-031" "PLT-TH1-033" "PLT-TH1-039"
## [16] "PLT-TH1-042" "PLT-TH1-044" "PLT-TH1-046" "PLT-TH1-048" "PLT-TH1-052"
## [21] "PLT-TH1-054" "PLT-TH1-055" "PLT-TH1-056" "PLT-TH1-057" "PLT-TH1-059"
## [26] "PLT-TH1-060" "PLT-TH1-061" "PLT-TH1-062" "PLT-TH1-063" "PLT-TH1-064"
## [31] "PLT-TH1-065" "PLT-TH1-067" "PLT-TH1-070" "PLT-TH1-071" "PLT-TH1-073"
## [36] "PLT-TH1-074" "PLT-TH1-076" "PLT-TH1-078" "PLT-TH1-079" "PLT-TH1-082"
## [41] "PLT-TH1-083" "PLT-TH1-084" "PLT-TH1-087" "PLT-TH1-089" "PLT-TH1-090"
## [46] "PLT-TH1-091" "PLT-TH1-092" "PLT-TH1-095" "PLT-TH1-096" "PLT-TH1-097"
## [51] "PLT-TH1-100" "PLT-TH1-101" "PLT-TH1-102" "PLT-TH1-103" "PLT-TH1-106"
## [56] "PLT-TH1-108" "PLT-TH1-109" "PLT-TH1-110" "PLT-TH1-112" "PLT-TH1-113"
## [61] "PLT-TH1-114" "PLT-TH1-117" "PLT-TH1-118" "PLT-TH1-119" "PLT-TH1-121"
## [66] "PLT-TH1-122" "PLT-TH1-125" "PLT-TH1-126" "PLT-TH1-127" "PLT-TH1-130"
## [71] "PLT-TH1-131" "PLT-TH1-132" "PLT-TH1-135" "PLT-TH1-136" "PLT-TH1-138"
## [76] "PLT-TH1-140" "PLT-TH1-143" "PLT-TH1-145" "PLT-TH1-146" "PLT-TH1-147"
## [81] "PLT-TH1-148" "PLT-TH1-150" "PLT-TH1-151" "PLT-TH1-152" "PLT-TH1-153"
## [86] "PLT-TH1-154" "PLT-TH1-155" "PLT-TH1-163" "PLT-TH1-164" "PLT-TH1-166"
## [91] "PLT-TH1-167" "PLT-TH1-169" "PLT-TH1-170" "PLT-TH1-171" "PLT-TH1-172"
## [96] "PLT-TH1-174" "PLT-TH1-177" "PLT-TH1-178" "PLT-TH1-181" "PLT-TH1-182"
## [101] "PLT-TH1-184" "PLT-TH1-186" "PLT-TH1-190" "PLT-TH1-192" "PLT-TH1-193"
## [106] "PLT-TH1-194" "PLT-TH1-196" "PLT-TH1-197" "PLT-TH1-198" "PLT-TH1-199"
## [111] "PLT-TH1-200" "PLT-TH1-201" "PLT-TH1-203" "PLT-TH1-204" "PLT-TH58-001"
## [116] "PLT-TH58-003" "PLT-TH58-005" "PLT-TH58-007" "PLT-TH58-009" "PLT-TH57-004"
## [121] "PLT-TH57-005" "PLT-TH57-006" "PLT-TH57-007" "PLT-TH57-008"
## [1] TRUE
## Negative time for following samples: PLT-TH57-003
## Negative time for following samples: PLT-TH57-009
## Negative time for following samples: PLT-TH57-010
## Negative time for following samples: PLT-TH58-004
## All negative samples for id: PLT-TH1-128
Display the per protocol matrix
## Number of patients per arm in modified intention to treat analysis
## Include_mITT
## FALSE TRUE
## Casirivimab/\nimdevimab 0 10
## Ivermectin 1 45
## No study drug 3 41
## Number of swabs per protocol per treatment
## PP_swabs
## 6 8 12 20
## Casirivimab/\nimdevimab 0 0 0 10
## Ivermectin 1 1 3 41
## No study drug 2 0 0 42
## We have 1992 PCR datapoints on 99 patients from 3 sites between 2021-09-30 and 2022-04-18
## [1] TRUE
## The analysis dataset contains 96 patients. The geometric mean baseline (defined as samples taken within 6 hours of randomisation) viral load was 361704 copies per mL (IQR: 78075 to 2777174; range from 63 to 80411194)
Summary table
## [1] "Ivermectin" "Casirivimab/\nimdevimab"
## [3] "No study drug"
| Arm | n | Age | Baseline viral load (log10) | Number of vaccine doses | Antibody+ (%) | Male (%) | th001 | th057 | th058 |
|---|---|---|---|---|---|---|---|---|---|
| Casirivimab/ | |||||||||
| imdevimab | 10 | 26.5 (18-31) | 5.5 (3.7-7.8) | 2 (0-3) | 50 | 20 | 10 | 0 | 0 |
| Ivermectin | 45 | 29 (19-45) | 5.7 (1.9-7.6) | 2 (0-4) | 78 | 47 | 41 | 2 | 2 |
| No study drug | 41 | 27 (20-43) | 5.5 (3-7.7) | 2 (2-4) | 90 | 44 | 36 | 3 | 2 |
## Plotting data for 96 individuals
Make stan data set.
Covariates that we use in model 2:
## Total number of datapoints up until day 8 is 1700
## Number of patients per arm in analysis:
##
## No study drug Casirivimab/\nimdevimab Ivermectin
## 41 10 45
## There are a total of 96 patients in the database with a total of 1700 PCRs analysable
## 7.12% of samples are below LOD
## check stan data formatting:
We fit a set of Bayesian hierarchical models.
There are three underlying stan models * Linear_model_basic.stan: vanilla student-t regression with left censoring at 0 and with individual random effects for slope and intercept; * Linear_model_RNaseP.stan: Same as before but with the RNaseP measurements; * Nonlinear_model_RNaseP.stan: Non-linear model (up and then down) with RNaseP adjustment.
Models 2 and 3 are combined with either informative priors or non-informative priors, and with or without full covariate adjustment (8 combinations). Model 1 is only run with informative priors and with only key covariates.
## [1] "Stan_models/Linear_model_basic.stan"
## [2] "Stan_models/Linear_model_RNaseP.stan"
## [3] "Stan_models/Nonlinear_model_RNaseP.stan"
## We are running all models with 4 chains and 5000 samples for each chain, discarding half for burn-in and thining every 10, thus giving a total of 1200 posterior samples per model.
Models are run on a remote cluster using the R script run_models.R’ and the bash script bmrc.sh. Each model is given a seed for reproducibility.
Load model fits:
## Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2563.1 32.6
## p_loo 168.5 4.5
## looic 5126.2 65.2
## ------
## Monte Carlo SE of elpd_loo is 0.5.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1693 99.6% 426
## (0.5, 0.7] (ok) 7 0.4% 704
## (0.7, 1] (bad) 0 0.0% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
##
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2537.0 32.2
## p_loo 169.6 4.6
## looic 5074.1 64.3
## ------
## Monte Carlo SE of elpd_loo is 0.5.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1698 99.9% 404
## (0.5, 0.7] (ok) 2 0.1% 488
## (0.7, 1] (bad) 0 0.0% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
##
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2509.7 32.6
## p_loo 216.3 6.1
## looic 5019.4 65.1
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1676 98.6% 219
## (0.5, 0.7] (ok) 23 1.4% 215
## (0.7, 1] (bad) 1 0.1% 319
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2537.5 32.2
## p_loo 169.5 4.5
## looic 5074.9 64.4
## ------
## Monte Carlo SE of elpd_loo is 0.5.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1697 99.8% 445
## (0.5, 0.7] (ok) 3 0.2% 431
## (0.7, 1] (bad) 0 0.0% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
##
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2510.2 32.7
## p_loo 217.5 6.1
## looic 5020.4 65.3
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1684 99.1% 261
## (0.5, 0.7] (ok) 11 0.6% 130
## (0.7, 1] (bad) 4 0.2% 141
## (1, Inf) (very bad) 1 0.1% 288
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2537.3 32.3
## p_loo 170.6 4.6
## looic 5074.7 64.5
## ------
## Monte Carlo SE of elpd_loo is 0.5.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1698 99.9% 374
## (0.5, 0.7] (ok) 2 0.1% 1156
## (0.7, 1] (bad) 0 0.0% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
##
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2510.7 32.7
## p_loo 218.3 6.1
## looic 5021.3 65.4
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1675 98.5% 352
## (0.5, 0.7] (ok) 21 1.2% 132
## (0.7, 1] (bad) 4 0.2% 338
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2538.9 32.3
## p_loo 172.1 4.6
## looic 5077.8 64.7
## ------
## Monte Carlo SE of elpd_loo is 0.5.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1696 99.8% 332
## (0.5, 0.7] (ok) 4 0.2% 769
## (0.7, 1] (bad) 0 0.0% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
##
## All Pareto k estimates are ok (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
##
## Computed from 1200 by 1700 log-likelihood matrix
##
## Estimate SE
## elpd_loo -2512.8 32.8
## p_loo 220.7 6.2
## looic 5025.7 65.6
## ------
## Monte Carlo SE of elpd_loo is NA.
##
## Pareto k diagnostic values:
## Count Pct. Min. n_eff
## (-Inf, 0.5] (good) 1675 98.5% 204
## (0.5, 0.7] (ok) 24 1.4% 134
## (0.7, 1] (bad) 1 0.1% 936
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
## elpd_diff se_diff
## model2 0.0 0.0
## model1 -27.4 9.0
## elpd_diff se_diff
## model2 0.0 0.0
## model1 -27.2 9.0
## elpd_diff se_diff
## model2 0.0 0.0
## model1 -26.7 9.1
## elpd_diff se_diff
## model2 0.0 0.0
## model1 -26.1 9.0
Posterior distributions over the treatment effects for the interventions. Red: no effect; blue: median inferred effect.
##
## *******************
## Mean estimated treatment effects (multiplicative):
## Casirivimab/\nimdevimab Ivermectin
## [1,] 1.548114 0.9034610
## [2,] 1.523211 0.9085989
## [3,] 1.456219 0.9079275
## [4,] 1.655142 0.9216786
## [5,] 1.529204 0.9089353
## [6,] 1.511075 0.9198498
## [7,] 1.442625 0.9107858
## [8,] 1.649500 0.9306607
## [9,] 1.494531 0.9115989
##
## *******************
## Probability of super-superiority:
## Casirivimab/\nimdevimab Ivermectin
## [1,] 94.8 2.7
## [2,] 95.5 2.1
## [3,] 93.2 1.5
## [4,] 97.5 4.2
## [5,] 95.1 1.9
## [6,] 92.9 2.7
## [7,] 92.2 1.6
## [8,] 97.2 4.6
## [9,] 92.7 1.9
Overall effects
## mean sd 2.5% 97.5% n_eff
## trt_effect[1] 0.42082037 0.18000138 0.06723114 0.76607278 1215.0412
## trt_effect[2] -0.09585150 0.11045454 -0.31760287 0.11183423 714.9721
## alpha_0 5.70475575 0.16384894 5.37658822 6.00774713 1072.5793
## beta_0 -0.38285654 0.04776558 -0.48664422 -0.30260132 1060.9486
## sigma_logvl 0.92111378 0.02860333 0.86297974 0.97579958 1203.0559
## sigmasq_u[1] 0.94172296 0.08192802 0.78784124 1.11467205 1252.0824
## sigmasq_u[2] 0.49375166 0.05594806 0.39692484 0.61615426 1244.5064
## t_dof 6.36524522 1.03705028 4.70147254 8.68303784 1218.9419
## gamma_rnasep 0.21260111 0.02898213 0.15517090 0.26782564 1264.5385
## slope_coefs[1] 0.04646932 0.15304557 -0.24656406 0.35617377 1259.6781
## slope_coefs[2] 0.03347912 0.13870962 -0.24348413 0.28651548 1040.3141
## slope_coefs[3] 0.34013473 0.21968844 -0.09551169 0.78132210 1266.0676
## slope_coefs[4] 0.06459665 0.25177572 -0.44811509 0.54125861 1227.7465
## intercept_coefs[1] -0.44623992 0.24176494 -0.92683279 0.03669462 1163.8724
## intercept_coefs[2] 0.55373702 0.21690775 0.13535158 0.97477534 1083.4873
## intercept_coefs[3] -0.28655870 0.33925957 -0.97229145 0.41115809 1315.7318
## intercept_coefs[4] -0.34183028 0.37350739 -1.03969406 0.38853144 1237.8437
## Rhat
## trt_effect[1] 0.9987007
## trt_effect[2] 1.0056810
## alpha_0 1.0022903
## beta_0 1.0013694
## sigma_logvl 1.0019461
## sigmasq_u[1] 0.9992692
## sigmasq_u[2] 0.9990045
## t_dof 1.0003107
## gamma_rnasep 0.9979734
## slope_coefs[1] 1.0014472
## slope_coefs[2] 1.0033287
## slope_coefs[3] 1.0002600
## slope_coefs[4] 1.0007402
## intercept_coefs[1] 1.0005099
## intercept_coefs[2] 1.0004774
## intercept_coefs[3] 0.9989030
## intercept_coefs[4] 1.0005374
## Under model 2 the change in rate of clearance for Ivermectin compared to no study drug is -9.14% (95%CI: -27.21% to 11.83%)
## Under model 2 the change in rate of clearance for Regeneron compared to no study drug is 52.32% (95%CI: 6.95% to 115.13%)
## The main 3 models with weakly informative priors:
Covariate effects on the intercept (baseline viral load) and slope (viral clearance):
## The following `from` values were not present in `x`: Age_scaled, Antibody_test, Symptom_onset, N_dose
## The following `from` values were not present in `x`: Age_scaled, Antibody_test, Symptom_onset, N_dose
Plot the absolute slope estimate for each individual over time
## The following `from` values were not present in `x`: PLT-TH1-018, PLT-TH1-020, PLT-TH1-105
## The following `from` values were not present in `x`: PLT-TH1-018, PLT-TH1-020, PLT-TH1-105
## The following `from` values were not present in `x`: PLT-TH1-018, PLT-TH1-020, PLT-TH1-105
## Slopes plot for model setting 2
## mod prior cov_matrices Niter Nwarmup Nthin
## 2 Stan_models/Linear_model_RNaseP.stan 1 1 5000 2000 10
## Nchain
## 2 4
## In the no study drug arm the mean clearance half life was 20.1 (range 7.1 to 41.4)
## In the Ivermectin arm the mean clearance half life was 21.5 (range 8.9 to 51.9)
## In the Regeneron arm the mean clearance half life was 12.7 (range 6.2 to 19.9)
## The model estimated population mean clearance half-life is 19.2 (95% CI 14.8-23.9)
changes in half life
## In the Ivermectin arm the mean change in half life is 1.93 (95% CI -2.13 to 6.57)
## In the Regeneron arm the mean change in half life is -6.53 (95% CI -12.01 to -1.06)
Plot the individual slope estimates by group
## The following `from` values were not present in `x`: PLT-TH1-018, PLT-TH1-020, PLT-TH1-105
## The following `from` values were not present in `x`: PLT-TH1-018, PLT-TH1-020, PLT-TH1-105
Some exploratory covariate analyses
##
## Call:
## lm(formula = t_12 ~ trt, data = trt_summary_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.071 -5.065 -1.746 4.714 30.331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.138 1.289 15.621 <2e-16 ***
## trtCasirivimab/\nimdevimab -7.412 2.911 -2.546 0.0125 *
## trtIvermectin 1.399 1.782 0.785 0.4344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.255 on 93 degrees of freedom
## Multiple R-squared: 0.09123, Adjusted R-squared: 0.07169
## F-statistic: 4.668 on 2 and 93 DF, p-value: 0.0117
##
## Call:
## lm(formula = rate_mean ~ AUC_ivm, data = trt_summary_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.61810 -0.06836 0.02267 0.12154 0.26675
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.042e-01 2.550e-02 -15.848 <2e-16 ***
## AUC_ivm -1.910e-07 3.298e-06 -0.058 0.954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1694 on 84 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 3.991e-05, Adjusted R-squared: -0.01186
## F-statistic: 0.003353 on 1 and 84 DF, p-value: 0.954
##
## Call:
## lm(formula = rate_mean ~ Cmax_ivm, data = trt_summary_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.61856 -0.06838 0.02325 0.12166 0.26689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.037e-01 2.551e-02 -15.824 <2e-16 ***
## Cmax_ivm -9.336e-06 1.117e-04 -0.084 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1694 on 84 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 8.314e-05, Adjusted R-squared: -0.01182
## F-statistic: 0.006984 on 1 and 84 DF, p-value: 0.9336
Illustrative PK plot
Individual plots colored by model
## The following `from` values were not present in `x`: PLT-TH1-018, PLT-TH1-020, PLT-TH1-105